Detail výsledku
Elliptical and Archimedean Copulas in Estimation of Distribution Algorithm with Model Migration
Schwarz Josef, doc. Ing., CSc., UPSY (FIT)
Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that are based on building
and sampling a probability model. Copula theory provides methods that simplify the estimation of a probability
model. An island-based version of copula-based EDA with probabilistic model migration (mCEDA) was
tested on a set of well-known standard optimization benchmarks in the continuous domain. We investigated
two families of copulas - Archimedean and elliptical. Experimental results confirm that this concept of model
migration (mCEDA) yields better convergence as compared with the sequential version (sCEDA) and other
recently published copula-based EDAs.
Estimation of Distribution Algorithms, Copula Theory, Parallel EDA, Island-based Model, Multivariate
Copula Sampling, Migration of Probabilistic Models.
@inproceedings{BUT119927,
author="Martin {Hyrš} and Josef {Schwarz}",
title="Elliptical and Archimedean Copulas in Estimation of Distribution Algorithm with Model Migration",
booktitle="Proceedings of the 7th International Joint Conference on Computational Intelligence (IJCCI 2015)",
year="2015",
pages="212--219",
publisher="SciTePress - Science and Technology Publications",
address="Lisbon",
isbn="978-989-758-157-1",
url="https://www.fit.vut.cz/research/publication/11013/"
}